1. Field of the Invention
The present invention relates to a method and system for predicting the future behavior of an individual or group engaged in a particular type of activity when there is little or no information on previous behavior of that specific individual or group under comparable conditions.
2. Description of Related Art
An age-old question faced by advertising and marketing professionals is how to ensure that their advertising and marketing materials reach the largest possible audience that is favorably disposed to purchase the products or services being promoted, so as to maximize the effectiveness of an advertising campaign. Clearly, advertising via mass media, such as in newspapers and magazines having wide circulation or on network television programming, will cause the message to be delivered to the largest number of consumers in the shortest possible period of time. However, it is very likely that only a miniscule percentage of the persons to whom these materials are exposed at any given time would be attentive to the contained message or would be in the market for the product or service in question, at the exact moment of exposure. Therefore, such approaches are both inefficient and costly, since the cost is generally based on the number of persons to whom the advertising will be delivered, irrespective of their potential interest.
Clearly, a more targeted approach would be more cost-efficient, i.e., one that is limited only to consumers who are likely to be favorably predisposed to the products or services being promoted. Although the marketing messages would be viewed by fewer persons, yet, from a statistical perspective, a higher percentage of those persons would be likely to be induced to take positive action in response to that message. This results in a markedly more effective use of an advertising budget. A consumer's perceived predisposition could be based, for example, on his/her: being a member of a particular socio-economic class; having a relevant occupation or hobby; living in a certain geographic area; having a family of a certain size; being a graduate of a particular type of school; having a certain ethnic background; subscribing to certain periodicals; or having bought similar products or services in the recent past.
Therefore, over the years, retailers and their advertising affiliates have spent considerable time and money in acquiring, analyzing and categorizing information from millions of individual consumers. Information is acquired using traditional methods such as in-person and telephone surveys, and, in more recent years, through more sophisticated methods such as monitoring use of customer “loyalty cards” in connection with purchases, and monitoring web-surfing activity over various on-line services that access the Internet using “cookies” or comparable data-gathering mechanisms. Through well-known statistical and probability-based modeling techniques, this archive of pertinent information can be analyzed and processed in various ways to identify an individual consumer's preferences or predispositions to become engaged in certain types of activities. By comparing information collected from individuals sharing common attributes, group preferences or predispositions similarly can be established.
This information then can be used to deliver targeted advertising content to those individual consumers, or similarly oriented groups of consumers, who are most likely to be influenced by the advertising message, whether by means of traditional direct mailings, telemarketing programs, or real-time banner advertisements visible on a computer monitor screen during a particular user's on-line surfing activities, or television ads received during the normal broadcast or during interactive sessions. This results in a more systematic, more controlled delivery of content over the life span of a particular advertising campaign.
However, to date relatively little, if any, targeted content delivery could be made to a given individual unless a meaningful amount of data on that individual's past activity, within a particular environment or setting, had been compiled. This is especially a problem in the case of on-line delivery of advertising messages, e.g., when the user is visiting a specific Internet web site, since there is a relatively limited window of opportunity during which to reach the on-line user before he/she either leaves the web site in question to visit another web site, or logs off entirely from the on-line service that provides the access to the web sites. If a person is a first-time visitor to a particular on-line environment (e.g., a particular web site), and if there is no past history of either how that person is known to act within that environment, or how that person reacts to situations that are typical of those found in that environment, then targeted content delivery, if attempted at all, would need to be based on broad assumptions that may not in fact be applicable to that person. Some reports estimate that as many as 80% of all visitors to a web site are “unknown” to that web site, i.e., the web site has no data whatsoever on those visitors.
Therefore, it would be desirable to provide a technique by which to identify tendencies or preferences of a particular user of an information delivery service, based on similarities between the user's present activities in connection with that service and the characteristics exhibited by other users of the same or similar information delivery services.
It would also be desirable to use the identified tendencies or preferences to anticipate or predict the user's future behavior in connection with the information delivery service.
It would also be desirable to use the identified tendencies or preferences to deliver targeted informational content to the user based on the identified tendencies or preferences.